The present disclosure relates to systems and methods for medical imaging, and more specifically, to systems and methods for segmentation of medical images.
Magnetic resonance (“MR”) imaging provides a higher resolution alternative to radiography for visualization of soft tissues for purposes of diagnosis and tracking of disease. In particular, MR images can be used to assess joint degeneration in clinical practice and osteoarthritis (“OA”) research studies. For example, three-dimensional maps of knee joints providing pixel-wise measurements of cartilage thickness may be used to assess disease-related and treatment-related changes in cartilage over time.
Traditional approaches for segmentation of biological tissues include manual contouring of several structures over multiple image sets. Specifically, to obtain quantitative measures of cartilage thickness of a knee joint from MR images, for example, the bone-cartilage interface and cartilage surface boundary needs to be segmented over the entire articulating surface. However, such manual segmentation is extremely time-consuming, and its efficiency and reproducibility is influenced by the level of human expertise.
Hence, it would be desirable to have systems and methods capable of semi-automated and fully-automated segmentation of biological tissues in an expedient and accurate fashion for purposes of medical analysis, such as diagnosis and tracking of OA progression via articular cartilage measures.